Getting Data
df_confirmed <- read_csv("https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_time_series/time_series_covid19_confirmed_global.csv")
df_deaths <- read_csv("https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_time_series/time_series_covid19_deaths_global.csv")
df_recovered <- read_csv("https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_time_series/time_series_covid19_recovered_global.csv")
df_confirmed <- df_confirmed%>%
pivot_longer(cols = c(-`Province/State`, -`Country/Region`, -Lat, -Long), names_to = "Date")
df_confirmed <- rename(df_confirmed, Confirmed = value)
df_deaths <- df_deaths%>%
pivot_longer(cols = c(-`Province/State`, -`Country/Region`, -Lat, -Long), names_to = "Date")
df_recovered <- df_recovered%>%
pivot_longer(cols = c(-`Province/State`, -`Country/Region`, -Lat, -Long), names_to = "Date")
covid <- df_confirmed %>%
left_join(select(df_deaths, -Lat, -Long, Deaths = value))%>%
left_join(select(df_recovered, -Lat, -Long, Recovered = value))
covid$Date <- lubridate::mdy(covid$Date)
covid <- covid%>%
group_by(`Country/Region`, `Province/State`)%>%
mutate(new_confirmed = Confirmed - lag(Confirmed, n=1, order_by = Date))%>%
mutate(new_deaths = Deaths - lag(Deaths, n=1, order_by = Date))%>%
mutate(new_recovered = Recovered - lag(Recovered, n=1, order_by = Date))%>%
ungroup()
covid[4000:4005, ]
## # A tibble: 6 x 11
## `Province/State` `Country/Region` Lat Long Date Confirmed Deaths
## <chr> <chr> <dbl> <dbl> <date> <dbl> <dbl>
## 1 Hainan China 19.2 110. 2020-01-31 52 1
## 2 Hainan China 19.2 110. 2020-02-01 62 1
## 3 Hainan China 19.2 110. 2020-02-02 64 1
## 4 Hainan China 19.2 110. 2020-02-03 72 1
## 5 Hainan China 19.2 110. 2020-02-04 80 1
## 6 Hainan China 19.2 110. 2020-02-05 99 1
## # ... with 4 more variables: Recovered <dbl>, new_confirmed <dbl>,
## # new_deaths <dbl>, new_recovered <dbl>
Analyse Data
q<- covid%>%
ungroup()%>%
group_by(Date)%>%
summarise(Confirmed = sum(Confirmed), Recovered=sum(Recovered, na.rm=TRUE), Deaths=sum(Deaths))%>%
ggplot(aes(Date, `All Cases`))+
geom_col(aes(x = Date, y = Confirmed))+
geom_col(aes(x = Date, y = Recovered), color = "green")+
geom_col(aes(x = Date, y = Deaths), color = "red")+
labs(title = "Total Cases Worldwide")
plotly::ggplotly(q)
covid%>%
filter(`Country/Region` %in% c("Germany", "US", "Italy", "China"))%>%
ggplot(aes(Date, `New Cases`))+
geom_col(aes(x = Date, y= new_confirmed))+
geom_line(aes(x = Date, y= new_recovered), color = "green", size = 1)+
geom_line(aes(x = Date, y= new_deaths), color = "red", size = 1)+
labs(title = "Daily Reportet New Cases")+
facet_wrap(~ `Country/Region`)

# alt.: facet_grid(rows = vars(`Country/Region`))